scholarly journals TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data

2021 ◽  
Author(s):  
Chenxi Sun ◽  
Shenda Hong ◽  
Moxian Song ◽  
Yen-Hsiu Chou ◽  
Yongyue Sun ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Time Series Data ◽  
Series Data ◽  
Echo State Network ◽  
Time Intervals ◽  
Time Encoding ◽  
Real World Datasets ◽  
Ordinary Time ◽  
Time Information

Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern in real-world applications. For more accurate prediction, methods had better grasp more data characteristics. Different from ordinary time series, ISTS is characterized by irregular time intervals of intra-series and different sampling rates of inter-series. However, existing methods have suboptimal predictions due to artificially introducing new dependencies in a time series and biasedly learning relations among time series when modeling these two characteristics. In this work, we propose a novel Time Encoding (TE) mechanism. TE can embed the time information as time vectors in the complex domain. It has the properties of absolute distance and relative distance under different sampling rates, which helps to represent two irregularities. Meanwhile, we create a new model named Time Encoding Echo State Network (TE-ESN). It is the first ESNs-based model that can process ISTS data. Besides, TE-ESN incorporates long short-term memories and series fusion to grasp horizontal and vertical relations. Experiments on one chaos system and three real-world datasets show that TE-ESN performs better than all baselines and has better reservoir property.

scholarly journals Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Alaa Sagheer ◽  
Mostafa Kotb
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Real World ◽  
Time Series Data ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Series Data ◽  
Recurrent Networks ◽  
Stacked Autoencoder ◽  
Real World Datasets

AbstractCurrently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of the recurrent neural network (RNN) and its variant long short-term memory (LSTM) have been proven to be more accurate than traditional statistical methods in modelling time series data. Despite the reported advantages of the deep LSTM model, its performance in modelling multivariate time series (MTS) data has not been satisfactory, particularly when attempting to process highly non-linear and long-interval MTS datasets. The reason is that the supervised learning approach initializes the neurons randomly in such recurrent networks, disabling the neurons that ultimately must properly learn the latent features of the correlated variables included in the MTS dataset. In this paper, we propose a pre-trained LSTM-based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep LSTM recurrent networks. For evaluation purposes, two different case studies that include real-world datasets are investigated, where the performance of the proposed approach compares favourably with the deep LSTM approach. In addition, the proposed approach outperforms several reference models investigating the same case studies. Overall, the experimental results clearly show that the unsupervised pre-training approach improves the performance of deep LSTM and leads to better and faster convergence than other models.

scholarly journals spiralize: an R Package for Visualizing Data on Spirals

2021 ◽  
Author(s):  
Zuguang Gu ◽  
Daniel Huebschmann
Keyword(s):  
Time Series ◽  
General Solution ◽  
Data Visualization ◽  
Real World ◽  
Time Series Data ◽  
R Package ◽  
Series Data ◽  
Periodic Patterns ◽  
Real World Datasets ◽  
High Level

Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here we present the R package spiralize that provides a general solution for visualizing data on spirals. spiralize implements numerous graphics functions so that self-defined high-level graphics can be easily implemented by users. The power of spiralize is demonstrated by five real world datasets.

scholarly journals Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

2018 ◽  
Vol 15 (147) ◽  
pp. 20180695 ◽  
Author(s):  
Simone Cenci ◽  
Serguei Saavedra
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Model Averaging ◽  
Biotic Interactions ◽  
Series Data ◽  
Time Intervals ◽  
Novel Approach ◽  
Data Generating Process

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

scholarly journals Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4683-4690 ◽  
Author(s):  
Shuheng Li ◽  
Dezhi Hong ◽  
Hongning Wang
Keyword(s):  
Time Series ◽  
Demand Response ◽  
Real World ◽  
Time Series Data ◽  
Metric Learning ◽  
Fault Detection And Diagnosis ◽  
Series Data ◽  
Smart Building ◽  
Deep Metric Learning ◽  
Detection And Diagnosis

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.

Sensors to Events: Semantic Modeling and Recognition of Events from Data Streams

2016 ◽  
Vol 10 (04) ◽  
pp. 461-501 ◽  
Author(s):  
Om Prasad Patri ◽  
Anand V. Panangadan ◽  
Vikrambhai S. Sorathia ◽  
Viktor K. Prasanna
Keyword(s):  
Time Series ◽  
Real World ◽  
Data Streams ◽  
Time Series Data ◽  
Semantic Representation ◽  
Expressive Power ◽  
Sensor Data ◽  
Series Data ◽  
Formal Approach ◽  
Semantic Computing

Detecting and responding to real-world events is an integral part of any enterprise or organization, but Semantic Computing has been largely underutilized for complex event processing (CEP) applications. A primary reason for this gap is the difference in the level of abstraction between the high-level semantic models for events and the low-level raw data values received from sensor data streams. In this work, we investigate the need for Semantic Computing in various aspects of CEP, and intend to bridge this gap by utilizing recent advances in time series analytics and machine learning. We build upon the Process-oriented Event Model, which provides a formal approach to model real-world objects and events, and specifies the process of moving from sensors to events. We extend this model to facilitate Semantic Computing and time series data mining directly over the sensor data, which provides the advantage of automatically learning the required background knowledge without domain expertise. We illustrate the expressive power of our model in case studies from diverse applications, with particular emphasis on non-intrusive load monitoring in smart energy grids. We also demonstrate that this powerful semantic representation is still highly accurate and performs at par with existing approaches for event detection and classification.

scholarly journals A Novel Deep Learning Approach for Anomaly Detection of Time Series Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiwei Ji ◽  
Jiaheng Gong ◽  
Jiarui Feng
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Short Term Memory ◽  
Training Data ◽  
Series Data ◽  
Real Time System ◽  
Statistical Strategy ◽  
Anomaly Classification ◽  
Real World Datasets

Anomalies in time series, also called “discord,” are the abnormal subsequences. The occurrence of anomalies in time series may indicate that some faults or disease will occur soon. Therefore, development of novel computational approaches for anomaly detection (discord search) in time series is of great significance for state monitoring and early warning of real-time system. Previous studies show that many algorithms were successfully developed and were used for anomaly classification, e.g., health monitoring, traffic detection, and intrusion detection. However, the anomaly detection of time series was not well studied. In this paper, we proposed a long short-term memory- (LSTM-) based anomaly detection method (LSTMAD) for discord search from univariate time series data. LSTMAD learns the structural features from normal (nonanomalous) training data and then performs anomaly detection via a statistical strategy based on the prediction error for observed data. In our experimental evaluation using public ECG datasets and real-world datasets, LSTMAD detects anomalies more accurately than other existing approaches in comparison.

scholarly journals Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression

2017 ◽  
Author(s):  
Pasan Karunaratne ◽  
Masud Moshtaghi ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Trevor Cohn
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Real World ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Series Data ◽  
Combination Methods ◽  
The University ◽  
The City ◽  
Source Of Information

In time-series forecasting, regression is a popular method, with Gaussian Process Regression widely held to be the state of the art. The versatility of Gaussian Processes has led to them being used in many varied application domains. However, though many real-world applications involve data which follows a working-week structure, where weekends exhibit substantially different behavior to weekdays, methods for explicit modelling of working-week effects in Gaussian Process Regression models have not been proposed. Not explicitly modelling the working week fails to incorporate a significant source of information which can be invaluable in forecasting scenarios. In this work we provide novel kernel-combination methods to explicitly model working-week effects in time-series data for more accurate predictions using Gaussian Process Regression. Further, we demonstrate that prediction accuracy can be improved by constraining the non-convex optimization process of finding optimal hyperparameter values. We validate the effectiveness of our methods by performing multi-step prediction on two real-world publicly available time-series datasets - one relating to electricity Smart Meter data of the University of Melbourne, and the other relating to the counts of pedestrians in the City of Melbourne.

scholarly journals Stochastic Online Anomaly Analysis for Streaming Time Series

2017 ◽  
Author(s):  
Zhao Xu ◽  
Kristian Kersting ◽  
Lorenzo von Ritter
Keyword(s):  
Time Series ◽  
Gradient Descent ◽  
Time Series Data ◽  
Anomalous Behavior ◽  
Stochastic Gradient Descent ◽  
Series Data ◽  
Segmentation Methods ◽  
Heavy Tailed ◽  
Anomaly Analysis ◽  
Real World Datasets

Identifying patterns in time series that exhibit anomalous behavior is of increasing importance in many domains, such as financial and Web data analysis. In real applications, time series data often arrive continuously, and usually only a single scan is allowed through the data. Batch learning and retrospective segmentation methods would not be well applicable to such scenarios. In this paper, we present an online nonparametric Bayesian method OLAD for anomaly analysis in streaming time series. Moreover, we develop a novel and efficient online learning approach for the OLAD model based on stochastic gradient descent. The proposed method can effectively learn the underlying dynamics of anomaly-contaminated heavy-tailed time series and identify potential anomalous events. Empirical analysis on real-world datasets demonstrates the effectiveness of our method.

Extreme Value Statistics

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Infectious Disease ◽  
New York ◽  
Time Series ◽  
Real World ◽  
Scarlet Fever ◽  
Time Series Data ◽  
Extreme Value Statistics ◽  
Series Data ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

This Capstone chapter illustrates how concepts in the book come together to diagnose real-world dynamics from observed time series data. In particular, we apply NLTS to diagnose multi-strain infectious disease dynamics from weekly cases of scarlet fever, measles, and pertussis in New York during the pre-vaccine period 1924-1948.

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